Geert Molenberghs is Professor of Biostatistics (Hasselt University, KULeuven. He works on surrogate endpoints, longitudinal and incomplete data, was Editor for Applied Statistics, Biometrics, Biostatistics, Wiley Probability & Statistics, and Wiley StatsRef and is Executive Editor of Biometrics. He was President of the International Biometric Society, is Fellow of the American Statistical Association, and received the Guy Medal in Bronze from the Royal Statistical Society. He has held visiting positions at the Harvard School of Public Health.

Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. He is a Fellow of the American Statistical Association and published on subgroup identification in clinical data, analysis with missing data, and causal inference. He is a frequent presenter at conferences, a co-developer of subgroup identification methods, and a co-author of the book "Analyzing Longitudinal Clinical Trial Data. A Practical Guide."

Bohdana Ratitch is a Principal Research Scientist at Eli Lilly and Company. Bohdana has contributed to research and practical applications of methodologies for causal inference and missing data in clinical trials through active participation in a pharma industry working group, numerous publications, presentations, and co-authoring the book "Clinical Trials with Missing Data: A Guide for Practitioners".

Craig Mallinckrodt holds the rank of Distinguished Biostatistician at Biogen in Cambridge MA. He has extensive experience in all phases of clinical research. His methodology research focuses on longitudinal and incomplete data. He is Fellow of the American Statistical Association, has led several industry working groups on missing and longitudinal data, and received the Royal Statistical Society's award for outstanding contribution to the pharmaceutical industry.

Section I Setting the Stage 1. Introduction 2. Why Are Estimands Important? Section II Estimands 3. Estimands and How to Define Them 4. Strategies for Dealing with Intercurrent Events 5. Examples from Actual Clinical Trials in Choosing and Specifying Estimands 6. Causal Inference and Estimands 7. Putting the Principles into Practice Section III Estimators and Sensitivity 8. Overview of Estimators 9. Modeling Considerations 10. Overview of Analyses for Composite Intercurrent Event Strategies 11. Overview of Analyses for Hypothetical Intercurrent Event Strategies 12. Overview of Analyses for Principal Stratification Intercurrent Event Strategies 13. Overview of Analyses for While-on-Treatment Intercurrent Event Strategies 14. Overview of Analyses for Treatment Policy Intercurrent Event Strategies 15. Missing Data 16. Sensitivity Analyses Section IV Technical Details on Selected Analyses 17. Example Data 18. Direct Maximum Likelihood 19. Multiple Imputation 20. Inverse Probability Weighted Generalized Estimated Equations 21. Doubly Robust Methods 22. Reference-Based Imputation 23. Delta Adjustment 24. Overview of Principal Stratification Methods Section V Case Studies: Detailed Analytic Examples 25. Analytic Case Study of Depression Clinical Trials 26. Analytic Case Study Based on the ACTG 175 HIV Trial